Estimating a social cost of carbon for global energy consumption.

Estimates of global economic damage caused by carbon dioxide (CO2) emissions can inform climate policy1-3. The social cost of carbon (SCC) quantifies these damages by characterizing how additional CO2 emissions today impact future economic outcomes through altering the climate4-6. Previous estimates have suggested that large, warming-driven increases in energy expenditures could dominate the SCC7,8, but they rely on models9-11 that are spatially coarse and not tightly linked to data2,3,6,7,12,13. Here we show that the release of one ton of CO2 today is projected to reduce total future energy expenditures, with most estimates valued between -US$3 and -US$1, depending on discount rates. Our results are based on an architecture that integrates global data, econometrics and climate science to estimate local damages worldwide. Notably, we project that emerging economies in the tropics will dramatically increase electricity consumption owing to warming, which requires critical infrastructure planning. However, heating reductions in colder countries offset this increase globally. We estimate that 2099 annual global electricity consumption increases by about 4.5 exajoules (7 per cent of current global consumption) per one-degree-Celsius increase in global mean surface temperature (GMST), whereas direct consumption of other fuels declines by about 11.3 exajoules (7 per cent of current global consumption) per one-degree-Celsius increase in GMST. Our finding of net savings contradicts previous research7,8, because global data indicate that many populations will remain too poor for most of the twenty-first century to substantially increase energy consumption in response to warming. Importantly, damage estimates would differ if poorer populations were given greater weight14.

Global evidence for ultraviolet radiation decreasing COVID-19 growth rates.

With nearly every country combating the 2019 novel coronavirus (COVID-19), there is a need to understand how local environmental conditions may modify transmission. To date, quantifying seasonality of the disease has been limited by scarce data and the difficulty of isolating climatological variables from other drivers of transmission in observational studies. We combine a spatially resolved dataset of confirmed COVID-19 cases, composed of 3,235 regions across 173 countries, with local environmental conditions and a statistical approach developed to quantify causal effects of environmental conditions in observational data settings. We find that ultraviolet (UV) radiation has a statistically significant effect on daily COVID-19 growth rates: a SD increase in UV lowers the daily growth rate of COVID-19 cases by ∼1 percentage point over the subsequent 2.5 wk, relative to an average in-sample growth rate of 13.2%. The time pattern of lagged effects peaks 9 to 11 d after UV exposure, consistent with the combined timescale of incubation, testing, and reporting. Cumulative effects of temperature and humidity are not statistically significant. Simulations illustrate how seasonal changes in UV have influenced regional patterns of COVID-19 growth rates from January to June, indicating that UV has a substantially smaller effect on the spread of the disease than social distancing policies. Furthermore, total COVID-19 seasonality has indeterminate sign for most regions during this period due to uncertain effects of other environmental variables. Our findings indicate UV exposure influences COVID-19 cases, but a comprehensive understanding of seasonality awaits further analysis.

Crop-damaging temperatures increase suicide rates in India.

More than three quarters of the world's suicides occur in developing countries, yet little is known about the drivers of suicidal behavior in poor populations. I study India, where one fifth of global suicides occur and suicide rates have doubled since 1980. Using nationally comprehensive panel data over 47 y, I demonstrate that fluctuations in climate, particularly temperature, significantly influence suicide rates. For temperatures above 20 °C, a 1 °C increase in a single day's temperature causes ∼70 suicides, on average. This effect occurs only during India's agricultural growing season, when heat also lowers crop yields. I find no evidence that acclimatization, rising incomes, or other unobserved drivers of adaptation are occurring. I estimate that warming over the last 30 y is responsible for 59,300 suicides in India, accounting for 6.8% of the total upward trend. These results deliver large-scale quantitative evidence linking climate and agricultural income to self-harm in a developing country.

Field-scale crop water consumption estimates reveal potential water savings in California agriculture.

Efficiently managing agricultural irrigation is vital for food security today and into the future under climate change. Yet, evaluating agriculture's hydrological impacts and strategies to reduce them remains challenging due to a lack of field-scale data on crop water consumption. Here, we develop a method to fill this gap using remote sensing and machine learning, and leverage it to assess water saving strategies in California's Central Valley. We find that switching to lower water intensity crops can reduce consumption by up to 93%, but this requires adopting uncommon crop types. Northern counties have substantially lower irrigation efficiencies than southern counties, suggesting another potential source of water savings. Other practices that do not alter land cover can save up to 11% of water consumption. These results reveal diverse approaches for achieving sustainable water use, emphasizing the potential of sub-field scale crop water consumption maps to guide water management in California and beyond.

A generalizable and accessible approach to machine learning with global satellite imagery.

Combining satellite imagery with machine learning (SIML) has the potential to address global challenges by remotely estimating socioeconomic and environmental conditions in data-poor regions, yet the resource requirements of SIML limit its accessibility and use. We show that a single encoding of satellite imagery can generalize across diverse prediction tasks (e.g., forest cover, house price, road length). Our method achieves accuracy competitive with deep neural networks at orders of magnitude lower computational cost, scales globally, delivers label super-resolution predictions, and facilitates characterizations of uncertainty. Since image encodings are shared across tasks, they can be centrally computed and distributed to unlimited researchers, who need only fit a linear regression to their own ground truth data in order to achieve state-of-the-art SIML performance.

Social and economic impacts of climate.

For centuries, thinkers have considered whether and how climatic conditions-such as temperature, rainfall, and violent storms-influence the nature of societies and the performance of economies. A multidisciplinary renaissance of quantitative empirical research is illuminating important linkages in the coupled climate-human system. We highlight key methodological innovations and results describing effects of climate on health, economics, conflict, migration, and demographics. Because of persistent "adaptation gaps," current climate conditions continue to play a substantial role in shaping modern society, and future climate changes will likely have additional impact. For example, we compute that temperature depresses current U.S. maize yields by ~48%, warming since 1980 elevated conflict risk in Africa by ~11%, and future warming may slow global economic growth rates by ~0.28 percentage points per year. In general, we estimate that the economic and social burden of current climates tends to be comparable in magnitude to the additional projected impact caused by future anthropogenic climate changes. Overall, findings from this literature point to climate as an important influence on the historical evolution of the global economy, they should inform how we respond to modern climatic conditions, and they can guide how we predict the consequences of future climate changes.